Keras: Keras framework training model saving and reloading

Experimental data MNIST

Train the model for the first time and save it

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 换one hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

# 创建模型，输入784个神经元，输出10个神经元
model = Sequential([
        Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
    ])

# 定义优化器
sgd = SGD(lr=0.2)

# 定义优化器，loss function，训练过程中计算准确率
model.compile(
    optimizer = sgd,
    loss = 'mse',
    metrics=['accuracy'],
)

# 训练模型
model.fit(x_train,y_train,batch_size=64,epochs=5)

# 评估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)

# 保存模型
model.save('model.h5')   # HDF5文件，pip install h5py

Load the first trained model and retrain

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.models import load_model
# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 换one hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

# 载入模型
model = load_model('model.h5')

# 评估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)

# 训练模型
model.fit(x_train,y_train,batch_size=64,epochs=2)

# 评估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)

# 保存参数，载入参数
model.save_weights('my_model_weights.h5')
model.load_weights('my_model_weights.h5')
# 保存网络结构，载入网络结构
from keras.models import model_from_json
json_string = model.to_json()
model = model_from_json(json_string)

print(json_string)